Krasnogor, Natalio <br/><br/>
A memetic algorithm with self-adaptive local search  :  TSP as a case study 

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<br/><br/> In this paper we introduce a promising hybridization scheme for a Memetic Algorithm (MA). Our MA is composed of two optimization processes, a Genetic Algorithm and a Monte Carlo method (MC). In contrast with other GA-Monte Carlo hybridized memetic algorithms, in our work the MC stage serves two purposes: -- when the population is diverse it acts like a local search procedure and -- when the population converges its goal is to diversify the search. To achieve this, the MC is self-adaptive based on observations from the underlying GA behavior; the GA controls the long-term optimization process. We present preliminary, yet statistically significant, results on the application of this approach to the TSP problem.We also comment it successful application to a molecular conformational problem: Protein Folding. 
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<br/><br/><label>Standard No.: </label>DIF-M6700 
<br/><br/><label>Subjects--Topical Terms: </label><br/>ALGORITMOS